Dynamic temporal normalization for deep learning in video understanding applications

A method for application of video recognition to input video, comprising:

• receiving a current standardized feature map from a current layer of a deep neural network, the current standardized feature map corresponding to a current time stamp of the input video;
• generating current transform parameters for linear transformation of the current standardized feature map based on prior transform parameters and a current feature map corresponding to the current standardized feature map, the prior transform parameters used to transform a prior standardized feature map to a prior normalized feature map for a prior time stamp of the input video;
• transforming the current standardized feature map to a current normalized feature map using the current transform parameters, wherein the current transform parameters include a hidden state parameter and a cell state parameter of a meta temporal relay applied to the prior transform parameters and the current feature map;
• and
• generating a video recognition output based on application of a subsequent layer of the deep neural network to the current normalized feature map, wherein the subsequent layer of the deep neural network is subsequent to the current layer of the deep neural network.

At least one non-transitory machine readable medium comprising machine-readable instructions that, in response to being executed on a device, cause the device to apply video recognition to input video by:

• receiving a current standardized feature map from a current layer of a deep neural network, the current standardized feature map corresponding to a current time stamp of the input video;
• generating current transform parameters for linear transformation of the current standardized feature map based on prior transform parameters and a current feature map corresponding to the current standardized feature map, the prior transform parameters used to transform a prior standardized feature map to a prior normalized feature map for a prior time stamp of the input video;
• transforming the current standardized feature map to a current normalized feature map using the current transform parameters, wherein the current transform parameters include a hidden state parameter and a cell state parameter of a meta temporal relay applied to the prior transform parameters and the current feature map;
• and
• generating a video recognition output based on application of a subsequent layer of the deep neural network to the current normalized feature map, wherein the subsequent layer of the deep neural network is subsequent to the current layer of the deep neural network.